Abstract

Background

Expert knowledge in journal articles is an important source of data for reconstructing
biological pathways and creating new hypotheses. An important need for medical research
is to integrate this data with high throughput sources to build useful models that
span several scales. Researchers traditionally use mental models of pathways to integrate
information and development new hypotheses. Unfortunately, the amount of information
is often overwhelming and these are inadequate for predicting the dynamic response
of complex pathways. Hierarchical computational models that allow exploration of semi-quantitative
dynamics are useful systems biology tools for theoreticians, experimentalists and
clinicians and may provide a means for cross-communication.

Results

A novel approach for biological pathway modeling based on hybrid intelligent systems
or soft computing technologies is presented here. Intelligent hybrid systems, which
refers to several related computing methods such as fuzzy logic, neural nets, genetic
algorithms, and statistical analysis, has become ubiquitous in engineering applications
for complex control system modeling and design. Biological pathways may be considered
to be complex control systems, which medicine tries to manipulate to achieve desired
results. Thus, hybrid intelligent systems may provide a useful tool for modeling biological
system dynamics and computational exploration of new drug targets. A new modeling
approach based on these methods is presented in the context of hedgehog regulation
of the cell cycle in granule cells. Code and input files can be found at the Bionet
website: www.chip.ord/~wbosl/Software/Bionet.

Conclusion

This paper presents the algorithmic methods needed for modeling complicated biochemical
dynamics using rule-based models to represent expert knowledge in the context of cell
cycle regulation and tumor growth. A notable feature of this modeling approach is
that it allows biologists to build complex models from their knowledge base without
the need to translate that knowledge into mathematical form. Dynamics on several levels,
from molecular pathways to tissue growth, are seamlessly integrated. A number of common
network motifs are examined and used to build a model of hedgehog regulation of the
cell cycle in cerebellar neurons, which is believed to play a key role in the etiology
of medulloblastoma, a devastating childhood brain cancer.